Overview

Dataset statistics

Number of variables28
Number of observations1723626
Missing cells230662
Missing cells (%)0.5%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory266.3 MiB
Average record size in memory162.0 B

Variable types

Categorical10
Numeric16
Text1
DateTime1

Alerts

addr_state has a high cardinality: 51 distinct valuesHigh cardinality
application_type is highly imbalanced (76.8%)Imbalance
emp_length has 109140 (6.3%) missing valuesMissing
emp_title has 121522 (7.1%) missing valuesMissing
annual_inc is highly skewed (γ1 = 517.5385092)Skewed
dti is highly skewed (γ1 = 29.57193785)Skewed
bc_util has 22674 (1.3%) zerosZeros
chargeoff_within_12_mths has 1709623 (99.2%) zerosZeros
delinq_2yrs has 1393559 (80.9%) zerosZeros
inq_last_6mths has 1022454 (59.3%) zerosZeros
last_fico_range_low has 46252 (2.7%) zerosZeros

Reproduction

Analysis started2024-07-08 02:51:42.921747
Analysis finished2024-07-08 02:52:18.370852
Duration35.45 seconds
Software versionydata-profiling v4.8.3
Download configurationconfig.json

Variables

addr_state
Categorical

HIGH CARDINALITY 

Distinct51
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size14.8 MiB
CA
243479 
TX
142374 
NY
139295 
FL
123796 
IL
 
67521
Other values (46)
1007161 

Length

Max length2
Median length2
Mean length2
Min length2

Characters and Unicode

Total characters3447252
Distinct characters24
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st rowNC
2nd rowTX
3rd rowMI
4th rowTX
5th rowNC

Common Values

ValueCountFrequency (%)
CA 243479
 
14.1%
TX 142374
 
8.3%
NY 139295
 
8.1%
FL 123796
 
7.2%
IL 67521
 
3.9%
NJ 61865
 
3.6%
PA 57868
 
3.4%
OH 56692
 
3.3%
GA 56194
 
3.3%
NC 48316
 
2.8%
Other values (41) 726226
42.1%

Length

2024-07-07T21:52:18.398167image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
ca 243479
 
14.1%
tx 142374
 
8.3%
ny 139295
 
8.1%
fl 123796
 
7.2%
il 67521
 
3.9%
nj 61865
 
3.6%
pa 57868
 
3.4%
oh 56692
 
3.3%
ga 56194
 
3.3%
nc 48316
 
2.8%
Other values (41) 726226
42.1%

Most occurring characters

ValueCountFrequency (%)
A 580883
16.9%
N 389300
11.3%
C 380244
11.0%
L 231861
 
6.7%
T 216961
 
6.3%
M 210067
 
6.1%
I 184275
 
5.3%
Y 159593
 
4.6%
O 158406
 
4.6%
X 142374
 
4.1%
Other values (14) 793288
23.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 3447252
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
A 580883
16.9%
N 389300
11.3%
C 380244
11.0%
L 231861
 
6.7%
T 216961
 
6.3%
M 210067
 
6.1%
I 184275
 
5.3%
Y 159593
 
4.6%
O 158406
 
4.6%
X 142374
 
4.1%
Other values (14) 793288
23.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 3447252
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
A 580883
16.9%
N 389300
11.3%
C 380244
11.0%
L 231861
 
6.7%
T 216961
 
6.3%
M 210067
 
6.1%
I 184275
 
5.3%
Y 159593
 
4.6%
O 158406
 
4.6%
X 142374
 
4.1%
Other values (14) 793288
23.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 3447252
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
A 580883
16.9%
N 389300
11.3%
C 380244
11.0%
L 231861
 
6.7%
T 216961
 
6.3%
M 210067
 
6.1%
I 184275
 
5.3%
Y 159593
 
4.6%
O 158406
 
4.6%
X 142374
 
4.1%
Other values (14) 793288
23.0%

annual_inc
Real number (ℝ)

SKEWED 

Distinct74968
Distinct (%)4.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean77830.805
Minimum0
Maximum1.1 × 108
Zeros4
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size26.3 MiB
2024-07-07T21:52:18.439509image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile28000
Q146500
median65000
Q392700
95-th percentile160000
Maximum1.1 × 108
Range1.1 × 108
Interquartile range (IQR)46200

Descriptive statistics

Standard deviation121155.26
Coefficient of variation (CV)1.5566491
Kurtosis430832.1
Mean77830.805
Median Absolute Deviation (MAD)21500
Skewness517.53851
Sum1.341512 × 1011
Variance1.4678596 × 1010
MonotonicityNot monotonic
2024-07-07T21:52:18.486641image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
60000 66595
 
3.9%
50000 58612
 
3.4%
65000 50309
 
2.9%
70000 47848
 
2.8%
80000 45837
 
2.7%
40000 45448
 
2.6%
75000 44824
 
2.6%
45000 42113
 
2.4%
55000 40028
 
2.3%
100000 35834
 
2.1%
Other values (74958) 1246178
72.3%
ValueCountFrequency (%)
0 4
< 0.1%
15 1
 
< 0.1%
16 1
 
< 0.1%
20 2
< 0.1%
23 1
 
< 0.1%
25 1
 
< 0.1%
32 1
 
< 0.1%
33 1
 
< 0.1%
39 1
 
< 0.1%
40 1
 
< 0.1%
ValueCountFrequency (%)
110000000 1
< 0.1%
61000000 1
< 0.1%
10999200 1
< 0.1%
9573072 1
< 0.1%
9550000 1
< 0.1%
9522972 1
< 0.1%
9500000 1
< 0.1%
9300000 1
< 0.1%
9225000 1
< 0.1%
9100000 1
< 0.1%

application_type
Categorical

IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size14.8 MiB
Individual
1658410 
Joint App
 
65216

Length

Max length10
Median length10
Mean length9.9621635
Min length9

Characters and Unicode

Total characters17171044
Distinct characters14
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowIndividual
2nd rowIndividual
3rd rowIndividual
4th rowIndividual
5th rowIndividual

Common Values

ValueCountFrequency (%)
Individual 1658410
96.2%
Joint App 65216
 
3.8%

Length

2024-07-07T21:52:18.528436image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-07-07T21:52:18.564928image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
ValueCountFrequency (%)
individual 1658410
92.7%
joint 65216
 
3.6%
app 65216
 
3.6%

Most occurring characters

ValueCountFrequency (%)
i 3382036
19.7%
d 3316820
19.3%
n 1723626
10.0%
I 1658410
9.7%
v 1658410
9.7%
u 1658410
9.7%
a 1658410
9.7%
l 1658410
9.7%
p 130432
 
0.8%
J 65216
 
0.4%
Other values (4) 260864
 
1.5%

Most occurring categories

ValueCountFrequency (%)
(unknown) 17171044
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
i 3382036
19.7%
d 3316820
19.3%
n 1723626
10.0%
I 1658410
9.7%
v 1658410
9.7%
u 1658410
9.7%
a 1658410
9.7%
l 1658410
9.7%
p 130432
 
0.8%
J 65216
 
0.4%
Other values (4) 260864
 
1.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 17171044
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
i 3382036
19.7%
d 3316820
19.3%
n 1723626
10.0%
I 1658410
9.7%
v 1658410
9.7%
u 1658410
9.7%
a 1658410
9.7%
l 1658410
9.7%
p 130432
 
0.8%
J 65216
 
0.4%
Other values (4) 260864
 
1.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 17171044
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
i 3382036
19.7%
d 3316820
19.3%
n 1723626
10.0%
I 1658410
9.7%
v 1658410
9.7%
u 1658410
9.7%
a 1658410
9.7%
l 1658410
9.7%
p 130432
 
0.8%
J 65216
 
0.4%
Other values (4) 260864
 
1.5%

avg_cur_bal
Real number (ℝ)

Distinct82765
Distinct (%)4.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean13476.81
Minimum0
Maximum555925
Zeros606
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size26.3 MiB
2024-07-07T21:52:18.601837image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1080
Q13084
median7368
Q318732.75
95-th percentile43192
Maximum555925
Range555925
Interquartile range (IQR)15648.75

Descriptive statistics

Standard deviation16166.264
Coefficient of variation (CV)1.1995616
Kurtosis28.730209
Mean13476.81
Median Absolute Deviation (MAD)5391
Skewness3.4866844
Sum2.3228981 × 1010
Variance2.6134809 × 108
MonotonicityNot monotonic
2024-07-07T21:52:18.645325image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 606
 
< 0.1%
1971 224
 
< 0.1%
2253 222
 
< 0.1%
2606 216
 
< 0.1%
2842 216
 
< 0.1%
2149 215
 
< 0.1%
2289 213
 
< 0.1%
2301 213
 
< 0.1%
2335 212
 
< 0.1%
2656 212
 
< 0.1%
Other values (82755) 1721077
99.9%
ValueCountFrequency (%)
0 606
< 0.1%
1 52
 
< 0.1%
2 46
 
< 0.1%
3 50
 
< 0.1%
4 26
 
< 0.1%
5 42
 
< 0.1%
6 38
 
< 0.1%
7 29
 
< 0.1%
8 28
 
< 0.1%
9 31
 
< 0.1%
ValueCountFrequency (%)
555925 1
< 0.1%
502002 1
< 0.1%
498284 1
< 0.1%
497484 1
< 0.1%
478909 1
< 0.1%
477255 1
< 0.1%
466840 1
< 0.1%
463945 1
< 0.1%
463276 1
< 0.1%
447102 1
< 0.1%

bc_util
Real number (ℝ)

ZEROS 

Distinct1473
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean58.153188
Minimum0
Maximum339.6
Zeros22674
Zeros (%)1.3%
Negative0
Negative (%)0.0%
Memory size26.3 MiB
2024-07-07T21:52:18.689527image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile7.4
Q135.6
median60.7
Q383.5
95-th percentile97.9
Maximum339.6
Range339.6
Interquartile range (IQR)47.9

Descriptive statistics

Standard deviation28.722617
Coefficient of variation (CV)0.49391302
Kurtosis-0.99238278
Mean58.153188
Median Absolute Deviation (MAD)23.8
Skewness-0.28610558
Sum1.0023435 × 108
Variance824.98872
MonotonicityNot monotonic
2024-07-07T21:52:18.736479image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 22674
 
1.3%
98 4806
 
0.3%
97 4508
 
0.3%
99 4446
 
0.3%
96 4348
 
0.3%
95 4054
 
0.2%
94 3860
 
0.2%
93 3651
 
0.2%
92 3559
 
0.2%
91 3432
 
0.2%
Other values (1463) 1664288
96.6%
ValueCountFrequency (%)
0 22674
1.3%
0.1 1873
 
0.1%
0.2 1650
 
0.1%
0.3 1385
 
0.1%
0.4 1211
 
0.1%
0.5 1155
 
0.1%
0.6 1059
 
0.1%
0.7 1037
 
0.1%
0.8 1004
 
0.1%
0.9 937
 
0.1%
ValueCountFrequency (%)
339.6 1
< 0.1%
318.2 1
< 0.1%
255.2 1
< 0.1%
252.3 1
< 0.1%
243.8 1
< 0.1%
235.3 1
< 0.1%
204.6 1
< 0.1%
202.9 1
< 0.1%
202 1
< 0.1%
201.9 1
< 0.1%

chargeoff_within_12_mths
Real number (ℝ)

ZEROS 

Distinct11
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.0091075442
Minimum0
Maximum10
Zeros1709623
Zeros (%)99.2%
Negative0
Negative (%)0.0%
Memory size26.3 MiB
2024-07-07T21:52:18.777780image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum10
Range10
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.10979191
Coefficient of variation (CV)12.055051
Kurtosis615.20166
Mean0.0091075442
Median Absolute Deviation (MAD)0
Skewness18.20006
Sum15698
Variance0.012054263
MonotonicityNot monotonic
2024-07-07T21:52:18.814970image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=11)
ValueCountFrequency (%)
0 1709623
99.2%
1 12787
 
0.7%
2 951
 
0.1%
3 157
 
< 0.1%
4 61
 
< 0.1%
5 20
 
< 0.1%
6 12
 
< 0.1%
7 6
 
< 0.1%
9 6
 
< 0.1%
8 2
 
< 0.1%
ValueCountFrequency (%)
0 1709623
99.2%
1 12787
 
0.7%
2 951
 
0.1%
3 157
 
< 0.1%
4 61
 
< 0.1%
5 20
 
< 0.1%
6 12
 
< 0.1%
7 6
 
< 0.1%
8 2
 
< 0.1%
9 6
 
< 0.1%
ValueCountFrequency (%)
10 1
 
< 0.1%
9 6
 
< 0.1%
8 2
 
< 0.1%
7 6
 
< 0.1%
6 12
 
< 0.1%
5 20
 
< 0.1%
4 61
 
< 0.1%
3 157
 
< 0.1%
2 951
 
0.1%
1 12787
0.7%

delinq_2yrs
Real number (ℝ)

ZEROS 

Distinct34
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.31646715
Minimum0
Maximum39
Zeros1393559
Zeros (%)80.9%
Negative0
Negative (%)0.0%
Memory size26.3 MiB
2024-07-07T21:52:18.853212image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile2
Maximum39
Range39
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.88001455
Coefficient of variation (CV)2.7807454
Kurtosis60.074987
Mean0.31646715
Median Absolute Deviation (MAD)0
Skewness5.6671897
Sum545471
Variance0.77442561
MonotonicityNot monotonic
2024-07-07T21:52:18.897635image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=34)
ValueCountFrequency (%)
0 1393559
80.9%
1 219498
 
12.7%
2 63848
 
3.7%
3 23322
 
1.4%
4 10486
 
0.6%
5 5268
 
0.3%
6 3011
 
0.2%
7 1638
 
0.1%
8 1002
 
0.1%
9 629
 
< 0.1%
Other values (24) 1365
 
0.1%
ValueCountFrequency (%)
0 1393559
80.9%
1 219498
 
12.7%
2 63848
 
3.7%
3 23322
 
1.4%
4 10486
 
0.6%
5 5268
 
0.3%
6 3011
 
0.2%
7 1638
 
0.1%
8 1002
 
0.1%
9 629
 
< 0.1%
ValueCountFrequency (%)
39 1
 
< 0.1%
36 1
 
< 0.1%
32 1
 
< 0.1%
30 1
 
< 0.1%
29 2
< 0.1%
28 1
 
< 0.1%
27 1
 
< 0.1%
26 3
< 0.1%
25 1
 
< 0.1%
24 1
 
< 0.1%

dti
Real number (ℝ)

SKEWED 

Distinct9209
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean18.739791
Minimum-1
Maximum999
Zeros957
Zeros (%)0.1%
Negative2
Negative (%)< 0.1%
Memory size26.3 MiB
2024-07-07T21:52:18.944741image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum-1
5-th percentile5.06
Q111.96
median17.87
Q324.51
95-th percentile33.63
Maximum999
Range1000
Interquartile range (IQR)12.55

Descriptive statistics

Standard deviation13.159036
Coefficient of variation (CV)0.70219757
Kurtosis1933.1568
Mean18.739791
Median Absolute Deviation (MAD)6.24
Skewness29.571938
Sum32300391
Variance173.16022
MonotonicityNot monotonic
2024-07-07T21:52:18.990210image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
19.2 1234
 
0.1%
18 1224
 
0.1%
14.4 1210
 
0.1%
16.8 1203
 
0.1%
13.2 1154
 
0.1%
15.6 1154
 
0.1%
20.4 1121
 
0.1%
12 1103
 
0.1%
21.6 1076
 
0.1%
10.8 1029
 
0.1%
Other values (9199) 1712118
99.3%
ValueCountFrequency (%)
-1 2
 
< 0.1%
0 957
0.1%
0.01 12
 
< 0.1%
0.02 23
 
< 0.1%
0.03 14
 
< 0.1%
0.04 9
 
< 0.1%
0.05 15
 
< 0.1%
0.06 25
 
< 0.1%
0.07 19
 
< 0.1%
0.08 21
 
< 0.1%
ValueCountFrequency (%)
999 84
< 0.1%
994.4 1
 
< 0.1%
991.57 1
 
< 0.1%
962.83 1
 
< 0.1%
962.12 1
 
< 0.1%
942.17 1
 
< 0.1%
917.87 1
 
< 0.1%
893.1 1
 
< 0.1%
886.77 1
 
< 0.1%
879.55 1
 
< 0.1%

emp_length
Categorical

MISSING 

Distinct11
Distinct (%)< 0.1%
Missing109140
Missing (%)6.3%
Memory size14.8 MiB
10+ years
571557 
2 years
155260 
< 1 year
141144 
3 years
137934 
1 year
113352 
Other values (6)
495239 

Length

Max length9
Median length8
Mean length7.72525
Min length6

Characters and Unicode

Total characters12472308
Distinct characters18
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row4 years
2nd row2 years
3rd row10+ years
4th row3 years
5th row4 years

Common Values

ValueCountFrequency (%)
10+ years 571557
33.2%
2 years 155260
 
9.0%
< 1 year 141144
 
8.2%
3 years 137934
 
8.0%
1 year 113352
 
6.6%
5 years 105963
 
6.1%
4 years 102600
 
6.0%
6 years 78203
 
4.5%
8 years 73749
 
4.3%
7 years 72300
 
4.2%
(Missing) 109140
 
6.3%

Length

2024-07-07T21:52:19.032230image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
years 1359990
40.4%
10 571557
17.0%
1 254496
 
7.6%
year 254496
 
7.6%
2 155260
 
4.6%
141144
 
4.2%
3 137934
 
4.1%
5 105963
 
3.1%
4 102600
 
3.0%
6 78203
 
2.3%
Other values (3) 208473
 
6.2%

Most occurring characters

ValueCountFrequency (%)
1755630
14.1%
y 1614486
12.9%
e 1614486
12.9%
a 1614486
12.9%
r 1614486
12.9%
s 1359990
10.9%
1 826053
6.6%
0 571557
 
4.6%
+ 571557
 
4.6%
2 155260
 
1.2%
Other values (8) 774317
6.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 12472308
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
1755630
14.1%
y 1614486
12.9%
e 1614486
12.9%
a 1614486
12.9%
r 1614486
12.9%
s 1359990
10.9%
1 826053
6.6%
0 571557
 
4.6%
+ 571557
 
4.6%
2 155260
 
1.2%
Other values (8) 774317
6.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 12472308
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
1755630
14.1%
y 1614486
12.9%
e 1614486
12.9%
a 1614486
12.9%
r 1614486
12.9%
s 1359990
10.9%
1 826053
6.6%
0 571557
 
4.6%
+ 571557
 
4.6%
2 155260
 
1.2%
Other values (8) 774317
6.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 12472308
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
1755630
14.1%
y 1614486
12.9%
e 1614486
12.9%
a 1614486
12.9%
r 1614486
12.9%
s 1359990
10.9%
1 826053
6.6%
0 571557
 
4.6%
+ 571557
 
4.6%
2 155260
 
1.2%
Other values (8) 774317
6.2%

emp_title
Text

MISSING 

Distinct408249
Distinct (%)25.5%
Missing121522
Missing (%)7.1%
Memory size26.3 MiB
2024-07-07T21:52:19.171349image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Length

Max length42
Median length32
Mean length15.586813
Min length1

Characters and Unicode

Total characters24971695
Distinct characters160
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique314396 ?
Unique (%)19.6%

Sample

1st rowProject Manager
2nd rowSurgical Technician
3rd rowTeam Leadern Customer Ops & Systems
4th rowSystems Engineer
5th rowAssistant Director - Human Resources
ValueCountFrequency (%)
manager 239065
 
7.3%
director 61854
 
1.9%
sales 56831
 
1.7%
assistant 56386
 
1.7%
supervisor 45918
 
1.4%
specialist 43425
 
1.3%
teacher 43380
 
1.3%
engineer 41648
 
1.3%
senior 41454
 
1.3%
analyst 40728
 
1.3%
Other values (82815) 2584673
79.4%
2024-07-07T21:52:19.380035image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
e 2676869
 
10.7%
r 2151348
 
8.6%
a 1949747
 
7.8%
1802391
 
7.2%
i 1724884
 
6.9%
n 1684663
 
6.7%
t 1542856
 
6.2%
o 1234681
 
4.9%
s 1215422
 
4.9%
c 1049633
 
4.2%
Other values (150) 7939201
31.8%

Most occurring categories

ValueCountFrequency (%)
(unknown) 24971695
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e 2676869
 
10.7%
r 2151348
 
8.6%
a 1949747
 
7.8%
1802391
 
7.2%
i 1724884
 
6.9%
n 1684663
 
6.7%
t 1542856
 
6.2%
o 1234681
 
4.9%
s 1215422
 
4.9%
c 1049633
 
4.2%
Other values (150) 7939201
31.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 24971695
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e 2676869
 
10.7%
r 2151348
 
8.6%
a 1949747
 
7.8%
1802391
 
7.2%
i 1724884
 
6.9%
n 1684663
 
6.7%
t 1542856
 
6.2%
o 1234681
 
4.9%
s 1215422
 
4.9%
c 1049633
 
4.2%
Other values (150) 7939201
31.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 24971695
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e 2676869
 
10.7%
r 2151348
 
8.6%
a 1949747
 
7.8%
1802391
 
7.2%
i 1724884
 
6.9%
n 1684663
 
6.7%
t 1542856
 
6.2%
o 1234681
 
4.9%
s 1215422
 
4.9%
c 1049633
 
4.2%
Other values (150) 7939201
31.8%

fico_range_high
Real number (ℝ)

Distinct38
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean701.16961
Minimum664
Maximum850
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size26.3 MiB
2024-07-07T21:52:19.445067image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum664
5-th percentile664
Q1674
median694
Q3719
95-th percentile769
Maximum850
Range186
Interquartile range (IQR)45

Descriptive statistics

Standard deviation32.469936
Coefficient of variation (CV)0.046308247
Kurtosis1.5546435
Mean701.16961
Median Absolute Deviation (MAD)20
Skewness1.2606512
Sum1.2085542 × 109
Variance1054.2967
MonotonicityNot monotonic
2024-07-07T21:52:19.490213image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=38)
ValueCountFrequency (%)
664 150527
 
8.7%
674 145804
 
8.5%
669 145274
 
8.4%
684 130463
 
7.6%
679 130351
 
7.6%
689 114602
 
6.6%
694 111721
 
6.5%
699 100380
 
5.8%
704 93685
 
5.4%
709 85096
 
4.9%
Other values (28) 515723
29.9%
ValueCountFrequency (%)
664 150527
8.7%
669 145274
8.4%
674 145804
8.5%
679 130351
7.6%
684 130463
7.6%
689 114602
6.6%
694 111721
6.5%
699 100380
5.8%
704 93685
5.4%
709 85096
4.9%
ValueCountFrequency (%)
850 294
 
< 0.1%
844 384
 
< 0.1%
839 598
 
< 0.1%
834 1039
 
0.1%
829 1552
 
0.1%
824 2072
 
0.1%
819 2828
0.2%
814 3354
0.2%
809 4694
0.3%
804 5511
0.3%

fico_range_low
Real number (ℝ)

Distinct38
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean697.16944
Minimum660
Maximum845
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size26.3 MiB
2024-07-07T21:52:19.533771image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum660
5-th percentile660
Q1670
median690
Q3715
95-th percentile765
Maximum845
Range185
Interquartile range (IQR)45

Descriptive statistics

Standard deviation32.469156
Coefficient of variation (CV)0.046572834
Kurtosis1.5531038
Mean697.16944
Median Absolute Deviation (MAD)20
Skewness1.2604289
Sum1.2016594 × 109
Variance1054.2461
MonotonicityNot monotonic
2024-07-07T21:52:19.579909image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=38)
ValueCountFrequency (%)
660 150527
 
8.7%
670 145804
 
8.5%
665 145274
 
8.4%
680 130463
 
7.6%
675 130351
 
7.6%
685 114602
 
6.6%
690 111721
 
6.5%
695 100380
 
5.8%
700 93685
 
5.4%
705 85096
 
4.9%
Other values (28) 515723
29.9%
ValueCountFrequency (%)
660 150527
8.7%
665 145274
8.4%
670 145804
8.5%
675 130351
7.6%
680 130463
7.6%
685 114602
6.6%
690 111721
6.5%
695 100380
5.8%
700 93685
5.4%
705 85096
4.9%
ValueCountFrequency (%)
845 294
 
< 0.1%
840 384
 
< 0.1%
835 598
 
< 0.1%
830 1039
 
0.1%
825 1552
 
0.1%
820 2072
 
0.1%
815 2828
0.2%
810 3354
0.2%
805 4694
0.3%
800 5511
0.3%

grade
Categorical

Distinct7
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size14.8 MiB
B
505036 
C
491944 
A
318334 
D
257183 
E
107703 
Other values (2)
 
43426

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1723626
Distinct characters7
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowB
2nd rowB
3rd rowB
4th rowA
5th rowB

Common Values

ValueCountFrequency (%)
B 505036
29.3%
C 491944
28.5%
A 318334
18.5%
D 257183
14.9%
E 107703
 
6.2%
F 33821
 
2.0%
G 9605
 
0.6%

Length

2024-07-07T21:52:19.623530image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-07-07T21:52:19.661332image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
ValueCountFrequency (%)
b 505036
29.3%
c 491944
28.5%
a 318334
18.5%
d 257183
14.9%
e 107703
 
6.2%
f 33821
 
2.0%
g 9605
 
0.6%

Most occurring characters

ValueCountFrequency (%)
B 505036
29.3%
C 491944
28.5%
A 318334
18.5%
D 257183
14.9%
E 107703
 
6.2%
F 33821
 
2.0%
G 9605
 
0.6%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1723626
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
B 505036
29.3%
C 491944
28.5%
A 318334
18.5%
D 257183
14.9%
E 107703
 
6.2%
F 33821
 
2.0%
G 9605
 
0.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1723626
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
B 505036
29.3%
C 491944
28.5%
A 318334
18.5%
D 257183
14.9%
E 107703
 
6.2%
F 33821
 
2.0%
G 9605
 
0.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1723626
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
B 505036
29.3%
C 491944
28.5%
A 318334
18.5%
D 257183
14.9%
E 107703
 
6.2%
F 33821
 
2.0%
G 9605
 
0.6%

home_ownership
Categorical

Distinct6
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size14.8 MiB
MORTGAGE
853277 
RENT
676739 
OWN
192344 
ANY
 
1178
NONE
 
44

Length

Max length8
Median length5
Mean length5.86794
Min length3

Characters and Unicode

Total characters10114134
Distinct characters11
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowRENT
2nd rowMORTGAGE
3rd rowOWN
4th rowMORTGAGE
5th rowRENT

Common Values

ValueCountFrequency (%)
MORTGAGE 853277
49.5%
RENT 676739
39.3%
OWN 192344
 
11.2%
ANY 1178
 
0.1%
NONE 44
 
< 0.1%
OTHER 44
 
< 0.1%

Length

2024-07-07T21:52:19.704666image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-07-07T21:52:19.743777image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
ValueCountFrequency (%)
mortgage 853277
49.5%
rent 676739
39.3%
own 192344
 
11.2%
any 1178
 
0.1%
none 44
 
< 0.1%
other 44
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
G 1706554
16.9%
E 1530104
15.1%
R 1530060
15.1%
T 1530060
15.1%
O 1045709
10.3%
N 870349
8.6%
A 854455
8.4%
M 853277
8.4%
W 192344
 
1.9%
Y 1178
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 10114134
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
G 1706554
16.9%
E 1530104
15.1%
R 1530060
15.1%
T 1530060
15.1%
O 1045709
10.3%
N 870349
8.6%
A 854455
8.4%
M 853277
8.4%
W 192344
 
1.9%
Y 1178
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 10114134
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
G 1706554
16.9%
E 1530104
15.1%
R 1530060
15.1%
T 1530060
15.1%
O 1045709
10.3%
N 870349
8.6%
A 854455
8.4%
M 853277
8.4%
W 192344
 
1.9%
Y 1178
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 10114134
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
G 1706554
16.9%
E 1530104
15.1%
R 1530060
15.1%
T 1530060
15.1%
O 1045709
10.3%
N 870349
8.6%
A 854455
8.4%
M 853277
8.4%
W 192344
 
1.9%
Y 1178
 
< 0.1%

inq_last_6mths
Real number (ℝ)

ZEROS 

Distinct9
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.60783836
Minimum0
Maximum8
Zeros1022454
Zeros (%)59.3%
Negative0
Negative (%)0.0%
Memory size26.3 MiB
2024-07-07T21:52:19.782387image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q31
95-th percentile2
Maximum8
Range8
Interquartile range (IQR)1

Descriptive statistics

Standard deviation0.89797356
Coefficient of variation (CV)1.477323
Kurtosis3.5514995
Mean0.60783836
Median Absolute Deviation (MAD)0
Skewness1.7561808
Sum1047686
Variance0.80635652
MonotonicityNot monotonic
2024-07-07T21:52:19.818707image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=9)
ValueCountFrequency (%)
0 1022454
59.3%
1 461303
26.8%
2 161656
 
9.4%
3 56813
 
3.3%
4 15236
 
0.9%
5 5301
 
0.3%
6 859
 
< 0.1%
7 3
 
< 0.1%
8 1
 
< 0.1%
ValueCountFrequency (%)
0 1022454
59.3%
1 461303
26.8%
2 161656
 
9.4%
3 56813
 
3.3%
4 15236
 
0.9%
5 5301
 
0.3%
6 859
 
< 0.1%
7 3
 
< 0.1%
8 1
 
< 0.1%
ValueCountFrequency (%)
8 1
 
< 0.1%
7 3
 
< 0.1%
6 859
 
< 0.1%
5 5301
 
0.3%
4 15236
 
0.9%
3 56813
 
3.3%
2 161656
 
9.4%
1 461303
26.8%
0 1022454
59.3%

installment
Real number (ℝ)

Distinct87955
Distinct (%)5.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean445.03
Minimum4.93
Maximum1719.83
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size26.3 MiB
2024-07-07T21:52:19.860894image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum4.93
5-th percentile110.33
Q1251.19
median377.41
Q3591.4
95-th percentile986.47
Maximum1719.83
Range1714.9
Interquartile range (IQR)340.21

Descriptive statistics

Standard deviation267.16719
Coefficient of variation (CV)0.60033523
Kurtosis0.70471138
Mean445.03
Median Absolute Deviation (MAD)157.65
Skewness1.0068004
Sum7.6706528 × 108
Variance71378.307
MonotonicityNot monotonic
2024-07-07T21:52:19.908749image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
301.15 3609
 
0.2%
332.1 3293
 
0.2%
327.34 3280
 
0.2%
361.38 2977
 
0.2%
451.73 2613
 
0.2%
602.3 2591
 
0.2%
329.72 2318
 
0.1%
318.79 2281
 
0.1%
312.86 2181
 
0.1%
392.81 2168
 
0.1%
Other values (87945) 1696315
98.4%
ValueCountFrequency (%)
4.93 1
< 0.1%
7.61 1
< 0.1%
14.01 1
< 0.1%
14.77 1
< 0.1%
19.4 1
< 0.1%
20.11 1
< 0.1%
23.26 1
< 0.1%
23.36 1
< 0.1%
25.81 1
< 0.1%
25.86 1
< 0.1%
ValueCountFrequency (%)
1719.83 2
 
< 0.1%
1717.63 1
 
< 0.1%
1715.42 2
 
< 0.1%
1714.54 5
< 0.1%
1691.28 2
 
< 0.1%
1676.23 2
 
< 0.1%
1671.88 2
 
< 0.1%
1670.15 1
 
< 0.1%
1664.57 1
 
< 0.1%
1647.03 1
 
< 0.1%

int_rate
Real number (ℝ)

Distinct395
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.13186785
Minimum0.0531
Maximum0.3099
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size26.3 MiB
2024-07-07T21:52:19.958737image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum0.0531
5-th percentile0.0649
Q10.0949
median0.1269
Q30.1601
95-th percentile0.2235
Maximum0.3099
Range0.2568
Interquartile range (IQR)0.0652

Descriptive statistics

Standard deviation0.048386808
Coefficient of variation (CV)0.36693406
Kurtosis0.50430623
Mean0.13186785
Median Absolute Deviation (MAD)0.0325
Skewness0.74298971
Sum227290.86
Variance0.0023412832
MonotonicityNot monotonic
2024-07-07T21:52:20.007050image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.1099 41298
 
2.4%
0.1199 40799
 
2.4%
0.0532 38914
 
2.3%
0.1399 34642
 
2.0%
0.1299 27678
 
1.6%
0.0789 27379
 
1.6%
0.0917 26922
 
1.6%
0.1699 26422
 
1.5%
0.1149 26326
 
1.5%
0.1561 24259
 
1.4%
Other values (385) 1408987
81.7%
ValueCountFrequency (%)
0.0531 3294
 
0.2%
0.0532 38914
2.3%
0.0593 1809
 
0.1%
0.06 575
 
< 0.1%
0.0603 9250
 
0.5%
0.0607 2223
 
0.1%
0.0608 2937
 
0.2%
0.0611 3834
 
0.2%
0.0619 1314
 
0.1%
0.0624 7456
 
0.4%
ValueCountFrequency (%)
0.3099 579
< 0.1%
0.3094 500
< 0.1%
0.3089 480
< 0.1%
0.3084 511
< 0.1%
0.3079 1049
0.1%
0.3075 714
< 0.1%
0.3074 314
 
< 0.1%
0.3065 638
< 0.1%
0.3049 348
 
< 0.1%
0.3017 775
< 0.1%
Distinct96
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size26.3 MiB
Minimum2012-08-01 00:00:00
Maximum2020-09-01 00:00:00
2024-07-07T21:52:20.054032image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-07-07T21:52:20.102475image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

last_fico_range_high
Real number (ℝ)

Distinct72
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean678.56434
Minimum0
Maximum850
Zeros178
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size26.3 MiB
2024-07-07T21:52:20.341234image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile519
Q1624
median694
Q3739
95-th percentile799
Maximum850
Range850
Interquartile range (IQR)115

Descriptive statistics

Standard deviation82.521206
Coefficient of variation (CV)0.12161147
Kurtosis-0.096922495
Mean678.56434
Median Absolute Deviation (MAD)50
Skewness-0.55267549
Sum1.1695911 × 109
Variance6809.7495
MonotonicityNot monotonic
2024-07-07T21:52:20.384928image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
709 52576
 
3.1%
694 52004
 
3.0%
714 51829
 
3.0%
699 51696
 
3.0%
704 51590
 
3.0%
719 51275
 
3.0%
724 49743
 
2.9%
684 47392
 
2.7%
689 46898
 
2.7%
499 46074
 
2.7%
Other values (62) 1222549
70.9%
ValueCountFrequency (%)
0 178
 
< 0.1%
499 46074
2.7%
504 9844
 
0.6%
509 10503
 
0.6%
514 11848
 
0.7%
519 12131
 
0.7%
524 13700
 
0.8%
529 13441
 
0.8%
534 15212
 
0.9%
539 15271
 
0.9%
ValueCountFrequency (%)
850 354
 
< 0.1%
844 847
 
< 0.1%
839 1591
 
0.1%
834 3157
 
0.2%
829 5231
 
0.3%
824 6753
0.4%
819 9793
0.6%
814 11379
0.7%
809 14106
0.8%
804 16641
1.0%

last_fico_range_low
Real number (ℝ)

ZEROS 

Distinct71
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean661.33278
Minimum0
Maximum845
Zeros46252
Zeros (%)2.7%
Negative0
Negative (%)0.0%
Memory size26.3 MiB
2024-07-07T21:52:20.429139image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile515
Q1620
median690
Q3735
95-th percentile795
Maximum845
Range845
Interquartile range (IQR)115

Descriptive statistics

Standard deviation133.9242
Coefficient of variation (CV)0.20250652
Kurtosis13.21953
Mean661.33278
Median Absolute Deviation (MAD)50
Skewness-3.1831876
Sum1.1398904 × 109
Variance17935.69
MonotonicityNot monotonic
2024-07-07T21:52:20.473175image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
705 52576
 
3.1%
690 52004
 
3.0%
710 51829
 
3.0%
695 51696
 
3.0%
700 51590
 
3.0%
715 51275
 
3.0%
720 49743
 
2.9%
680 47392
 
2.7%
685 46898
 
2.7%
0 46252
 
2.7%
Other values (61) 1222371
70.9%
ValueCountFrequency (%)
0 46252
2.7%
500 9844
 
0.6%
505 10503
 
0.6%
510 11848
 
0.7%
515 12131
 
0.7%
520 13700
 
0.8%
525 13441
 
0.8%
530 15212
 
0.9%
535 15271
 
0.9%
540 17076
 
1.0%
ValueCountFrequency (%)
845 354
 
< 0.1%
840 847
 
< 0.1%
835 1591
 
0.1%
830 3157
 
0.2%
825 5231
 
0.3%
820 6753
0.4%
815 9793
0.6%
810 11379
0.7%
805 14106
0.8%
800 16641
1.0%

loan_amnt
Real number (ℝ)

Distinct1561
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean14760.653
Minimum1000
Maximum40000
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size26.3 MiB
2024-07-07T21:52:20.516826image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum1000
5-th percentile3200
Q18000
median12300
Q320000
95-th percentile35000
Maximum40000
Range39000
Interquartile range (IQR)12000

Descriptive statistics

Standard deviation8988.3744
Coefficient of variation (CV)0.60894153
Kurtosis-0.092056105
Mean14760.653
Median Absolute Deviation (MAD)5800
Skewness0.7846035
Sum2.5441845 × 1010
Variance80790874
MonotonicityNot monotonic
2024-07-07T21:52:20.566170image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
10000 135539
 
7.9%
20000 94864
 
5.5%
15000 91914
 
5.3%
12000 91430
 
5.3%
35000 65192
 
3.8%
5000 64124
 
3.7%
8000 59372
 
3.4%
6000 56016
 
3.2%
16000 48573
 
2.8%
25000 46861
 
2.7%
Other values (1551) 969741
56.3%
ValueCountFrequency (%)
1000 7535
0.4%
1025 29
 
< 0.1%
1050 43
 
< 0.1%
1075 20
 
< 0.1%
1100 216
 
< 0.1%
1125 36
 
< 0.1%
1150 33
 
< 0.1%
1175 14
 
< 0.1%
1200 2964
 
0.2%
1225 16
 
< 0.1%
ValueCountFrequency (%)
40000 17924
1.0%
39975 10
 
< 0.1%
39950 5
 
< 0.1%
39925 6
 
< 0.1%
39900 15
 
< 0.1%
39875 5
 
< 0.1%
39850 4
 
< 0.1%
39825 10
 
< 0.1%
39800 9
 
< 0.1%
39775 9
 
< 0.1%

loan_status
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size14.8 MiB
Fully Paid
1376874 
Non-Performing
346752 

Length

Max length14
Median length10
Mean length10.804704
Min length10

Characters and Unicode

Total characters18623268
Distinct characters18
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowFully Paid
2nd rowFully Paid
3rd rowFully Paid
4th rowFully Paid
5th rowFully Paid

Common Values

ValueCountFrequency (%)
Fully Paid 1376874
79.9%
Non-Performing 346752
 
20.1%

Length

2024-07-07T21:52:20.615302image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-07-07T21:52:20.652573image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
ValueCountFrequency (%)
fully 1376874
44.4%
paid 1376874
44.4%
non-performing 346752
 
11.2%

Most occurring characters

ValueCountFrequency (%)
l 2753748
14.8%
P 1723626
9.3%
i 1723626
9.3%
F 1376874
 
7.4%
y 1376874
 
7.4%
1376874
 
7.4%
a 1376874
 
7.4%
d 1376874
 
7.4%
u 1376874
 
7.4%
r 693504
 
3.7%
Other values (8) 3467520
18.6%

Most occurring categories

ValueCountFrequency (%)
(unknown) 18623268
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
l 2753748
14.8%
P 1723626
9.3%
i 1723626
9.3%
F 1376874
 
7.4%
y 1376874
 
7.4%
1376874
 
7.4%
a 1376874
 
7.4%
d 1376874
 
7.4%
u 1376874
 
7.4%
r 693504
 
3.7%
Other values (8) 3467520
18.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 18623268
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
l 2753748
14.8%
P 1723626
9.3%
i 1723626
9.3%
F 1376874
 
7.4%
y 1376874
 
7.4%
1376874
 
7.4%
a 1376874
 
7.4%
d 1376874
 
7.4%
u 1376874
 
7.4%
r 693504
 
3.7%
Other values (8) 3467520
18.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 18623268
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
l 2753748
14.8%
P 1723626
9.3%
i 1723626
9.3%
F 1376874
 
7.4%
y 1376874
 
7.4%
1376874
 
7.4%
a 1376874
 
7.4%
d 1376874
 
7.4%
u 1376874
 
7.4%
r 693504
 
3.7%
Other values (8) 3467520
18.6%

purpose
Categorical

Distinct14
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size14.8 MiB
debt_consolidation
992526 
credit_card
390604 
home_improvement
112479 
other
101545 
major_purchase
 
36821
Other values (9)
 
89651

Length

Max length18
Median length18
Mean length14.882855
Min length3

Characters and Unicode

Total characters25652475
Distinct characters22
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowdebt_consolidation
2nd rowhome_improvement
3rd rowdebt_consolidation
4th rowdebt_consolidation
5th rowdebt_consolidation

Common Values

ValueCountFrequency (%)
debt_consolidation 992526
57.6%
credit_card 390604
 
22.7%
home_improvement 112479
 
6.5%
other 101545
 
5.9%
major_purchase 36821
 
2.1%
medical 20170
 
1.2%
car 17095
 
1.0%
small_business 16957
 
1.0%
vacation 11825
 
0.7%
moving 11594
 
0.7%
Other values (4) 12010
 
0.7%

Length

2024-07-07T21:52:20.693199image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
debt_consolidation 992526
57.6%
credit_card 390604
 
22.7%
home_improvement 112479
 
6.5%
other 101545
 
5.9%
major_purchase 36821
 
2.1%
medical 20170
 
1.2%
car 17095
 
1.0%
small_business 16957
 
1.0%
vacation 11825
 
0.7%
moving 11594
 
0.7%
Other values (4) 12010
 
0.7%

Most occurring characters

ValueCountFrequency (%)
o 3374406
13.2%
d 2788164
10.9%
t 2601507
10.1%
i 2549549
9.9%
n 2140893
8.3%
e 1912306
7.5%
c 1859647
7.2%
_ 1550446
 
6.0%
a 1535707
 
6.0%
s 1107258
 
4.3%
Other values (12) 4232592
16.5%

Most occurring categories

ValueCountFrequency (%)
(unknown) 25652475
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
o 3374406
13.2%
d 2788164
10.9%
t 2601507
10.1%
i 2549549
9.9%
n 2140893
8.3%
e 1912306
7.5%
c 1859647
7.2%
_ 1550446
 
6.0%
a 1535707
 
6.0%
s 1107258
 
4.3%
Other values (12) 4232592
16.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 25652475
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
o 3374406
13.2%
d 2788164
10.9%
t 2601507
10.1%
i 2549549
9.9%
n 2140893
8.3%
e 1912306
7.5%
c 1859647
7.2%
_ 1550446
 
6.0%
a 1535707
 
6.0%
s 1107258
 
4.3%
Other values (12) 4232592
16.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 25652475
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
o 3374406
13.2%
d 2788164
10.9%
t 2601507
10.1%
i 2549549
9.9%
n 2140893
8.3%
e 1912306
7.5%
c 1859647
7.2%
_ 1550446
 
6.0%
a 1535707
 
6.0%
s 1107258
 
4.3%
Other values (12) 4232592
16.5%

revol_bal
Real number (ℝ)

Distinct92466
Distinct (%)5.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean16487.053
Minimum0
Maximum2904836
Zeros6602
Zeros (%)0.4%
Negative0
Negative (%)0.0%
Memory size26.3 MiB
2024-07-07T21:52:20.736886image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1701
Q15972
median11219
Q319980
95-th percentile44335
Maximum2904836
Range2904836
Interquartile range (IQR)14008

Descriptive statistics

Standard deviation22643.897
Coefficient of variation (CV)1.3734351
Kurtosis589.10068
Mean16487.053
Median Absolute Deviation (MAD)6237
Skewness12.648816
Sum2.8417513 × 1010
Variance5.1274607 × 108
MonotonicityNot monotonic
2024-07-07T21:52:20.784087image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 6602
 
0.4%
8 170
 
< 0.1%
6312 134
 
< 0.1%
5453 131
 
< 0.1%
2 128
 
< 0.1%
5891 123
 
< 0.1%
5232 123
 
< 0.1%
5849 123
 
< 0.1%
6118 122
 
< 0.1%
5570 122
 
< 0.1%
Other values (92456) 1715848
99.5%
ValueCountFrequency (%)
0 6602
0.4%
1 91
 
< 0.1%
2 128
 
< 0.1%
3 116
 
< 0.1%
4 112
 
< 0.1%
5 107
 
< 0.1%
6 113
 
< 0.1%
7 94
 
< 0.1%
8 170
 
< 0.1%
9 112
 
< 0.1%
ValueCountFrequency (%)
2904836 1
< 0.1%
2568995 1
< 0.1%
2560703 1
< 0.1%
1746716 1
< 0.1%
1743266 1
< 0.1%
1696796 1
< 0.1%
1470945 1
< 0.1%
1392002 1
< 0.1%
1298783 1
< 0.1%
1190046 1
< 0.1%

revol_util
Real number (ℝ)

Distinct1302
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.50486359
Minimum0
Maximum3.666
Zeros8029
Zeros (%)0.5%
Negative0
Negative (%)0.0%
Memory size26.3 MiB
2024-07-07T21:52:20.830940image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.096
Q10.318
median0.505
Q30.694
95-th percentile0.91
Maximum3.666
Range3.666
Interquartile range (IQR)0.376

Descriptive statistics

Standard deviation0.24626981
Coefficient of variation (CV)0.48779475
Kurtosis-0.80767585
Mean0.50486359
Median Absolute Deviation (MAD)0.188
Skewness-0.011133263
Sum870196.02
Variance0.060648819
MonotonicityNot monotonic
2024-07-07T21:52:20.881125image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 8029
 
0.5%
0.48 3358
 
0.2%
0.57 3346
 
0.2%
0.58 3314
 
0.2%
0.59 3313
 
0.2%
0.53 3298
 
0.2%
0.61 3277
 
0.2%
0.55 3246
 
0.2%
0.54 3228
 
0.2%
0.46 3216
 
0.2%
Other values (1292) 1686001
97.8%
ValueCountFrequency (%)
0 8029
0.5%
0.001 1329
 
0.1%
0.002 1089
 
0.1%
0.003 1000
 
0.1%
0.004 880
 
0.1%
0.005 833
 
< 0.1%
0.006 763
 
< 0.1%
0.007 740
 
< 0.1%
0.008 727
 
< 0.1%
0.009 705
 
< 0.1%
ValueCountFrequency (%)
3.666 1
< 0.1%
1.93 1
< 0.1%
1.846 1
< 0.1%
1.828 1
< 0.1%
1.803 1
< 0.1%
1.777 1
< 0.1%
1.72 1
< 0.1%
1.669 1
< 0.1%
1.658 1
< 0.1%
1.62 1
< 0.1%

sub_grade
Categorical

Distinct35
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size14.8 MiB
C1
 
109536
B4
 
107459
B5
 
107087
B3
 
100642
C2
 
100128
Other values (30)
1198774 

Length

Max length2
Median length2
Mean length2
Min length2

Characters and Unicode

Total characters3447252
Distinct characters12
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowB2
2nd rowB2
3rd rowB2
4th rowA3
5th rowB4

Common Values

ValueCountFrequency (%)
C1 109536
 
6.4%
B4 107459
 
6.2%
B5 107087
 
6.2%
B3 100642
 
5.8%
C2 100128
 
5.8%
C3 97262
 
5.6%
C4 96530
 
5.6%
B2 95260
 
5.5%
B1 94588
 
5.5%
C5 88488
 
5.1%
Other values (25) 726646
42.2%

Length

2024-07-07T21:52:20.926313image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
c1 109536
 
6.4%
b4 107459
 
6.2%
b5 107087
 
6.2%
b3 100642
 
5.8%
c2 100128
 
5.8%
c3 97262
 
5.6%
c4 96530
 
5.6%
b2 95260
 
5.5%
b1 94588
 
5.5%
c5 88488
 
5.1%
Other values (25) 726646
42.2%

Most occurring characters

ValueCountFrequency (%)
B 505036
14.7%
C 491944
14.3%
1 372403
10.8%
4 343145
10.0%
2 339603
9.9%
5 338462
9.8%
3 330013
9.6%
A 318334
9.2%
D 257183
7.5%
E 107703
 
3.1%
Other values (2) 43426
 
1.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 3447252
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
B 505036
14.7%
C 491944
14.3%
1 372403
10.8%
4 343145
10.0%
2 339603
9.9%
5 338462
9.8%
3 330013
9.6%
A 318334
9.2%
D 257183
7.5%
E 107703
 
3.1%
Other values (2) 43426
 
1.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 3447252
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
B 505036
14.7%
C 491944
14.3%
1 372403
10.8%
4 343145
10.0%
2 339603
9.9%
5 338462
9.8%
3 330013
9.6%
A 318334
9.2%
D 257183
7.5%
E 107703
 
3.1%
Other values (2) 43426
 
1.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 3447252
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
B 505036
14.7%
C 491944
14.3%
1 372403
10.8%
4 343145
10.0%
2 339603
9.9%
5 338462
9.8%
3 330013
9.6%
A 318334
9.2%
D 257183
7.5%
E 107703
 
3.1%
Other values (2) 43426
 
1.3%

term
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size14.8 MiB
36 months
1284233 
60 months
439393 

Length

Max length10
Median length10
Mean length10
Min length10

Characters and Unicode

Total characters17236260
Distinct characters10
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row 36 months
2nd row 36 months
3rd row 36 months
4th row 36 months
5th row 36 months

Common Values

ValueCountFrequency (%)
36 months 1284233
74.5%
60 months 439393
 
25.5%

Length

2024-07-07T21:52:20.963028image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-07-07T21:52:20.997704image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
ValueCountFrequency (%)
months 1723626
50.0%
36 1284233
37.3%
60 439393
 
12.7%

Most occurring characters

ValueCountFrequency (%)
3447252
20.0%
6 1723626
10.0%
m 1723626
10.0%
o 1723626
10.0%
n 1723626
10.0%
t 1723626
10.0%
h 1723626
10.0%
s 1723626
10.0%
3 1284233
 
7.5%
0 439393
 
2.5%

Most occurring categories

ValueCountFrequency (%)
(unknown) 17236260
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
3447252
20.0%
6 1723626
10.0%
m 1723626
10.0%
o 1723626
10.0%
n 1723626
10.0%
t 1723626
10.0%
h 1723626
10.0%
s 1723626
10.0%
3 1284233
 
7.5%
0 439393
 
2.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 17236260
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
3447252
20.0%
6 1723626
10.0%
m 1723626
10.0%
o 1723626
10.0%
n 1723626
10.0%
t 1723626
10.0%
h 1723626
10.0%
s 1723626
10.0%
3 1284233
 
7.5%
0 439393
 
2.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 17236260
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
3447252
20.0%
6 1723626
10.0%
m 1723626
10.0%
o 1723626
10.0%
n 1723626
10.0%
t 1723626
10.0%
h 1723626
10.0%
s 1723626
10.0%
3 1284233
 
7.5%
0 439393
 
2.5%
Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size14.8 MiB
Source Verified
688888 
Not Verified
539198 
Verified
495540 

Length

Max length15
Median length12
Mean length12.049027
Min length8

Characters and Unicode

Total characters20768016
Distinct characters13
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowNot Verified
2nd rowSource Verified
3rd rowVerified
4th rowNot Verified
5th rowNot Verified

Common Values

ValueCountFrequency (%)
Source Verified 688888
40.0%
Not Verified 539198
31.3%
Verified 495540
28.7%

Length

2024-07-07T21:52:21.039805image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-07-07T21:52:21.077734image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
ValueCountFrequency (%)
verified 1723626
58.4%
source 688888
 
23.3%
not 539198
 
18.3%

Most occurring characters

ValueCountFrequency (%)
e 4136140
19.9%
i 3447252
16.6%
r 2412514
11.6%
V 1723626
8.3%
f 1723626
8.3%
d 1723626
8.3%
o 1228086
 
5.9%
1228086
 
5.9%
S 688888
 
3.3%
u 688888
 
3.3%
Other values (3) 1767284
8.5%

Most occurring categories

ValueCountFrequency (%)
(unknown) 20768016
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e 4136140
19.9%
i 3447252
16.6%
r 2412514
11.6%
V 1723626
8.3%
f 1723626
8.3%
d 1723626
8.3%
o 1228086
 
5.9%
1228086
 
5.9%
S 688888
 
3.3%
u 688888
 
3.3%
Other values (3) 1767284
8.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 20768016
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e 4136140
19.9%
i 3447252
16.6%
r 2412514
11.6%
V 1723626
8.3%
f 1723626
8.3%
d 1723626
8.3%
o 1228086
 
5.9%
1228086
 
5.9%
S 688888
 
3.3%
u 688888
 
3.3%
Other values (3) 1767284
8.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 20768016
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e 4136140
19.9%
i 3447252
16.6%
r 2412514
11.6%
V 1723626
8.3%
f 1723626
8.3%
d 1723626
8.3%
o 1228086
 
5.9%
1228086
 
5.9%
S 688888
 
3.3%
u 688888
 
3.3%
Other values (3) 1767284
8.5%

Interactions

2024-07-07T21:52:11.372006image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-07-07T21:51:48.567625image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-07-07T21:51:50.005541image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-07-07T21:51:51.497557image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-07-07T21:51:53.128688image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-07-07T21:51:54.500975image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-07-07T21:51:55.988244image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-07-07T21:51:57.392579image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-07-07T21:51:59.032716image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-07-07T21:52:00.589035image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-07-07T21:52:01.954010image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-07-07T21:52:03.517720image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-07-07T21:52:05.230795image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-07-07T21:52:06.762123image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-07-07T21:52:08.299301image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-07-07T21:52:09.828159image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-07-07T21:52:11.470699image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-07-07T21:51:48.668533image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-07-07T21:51:50.095132image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-07-07T21:51:51.595838image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-07-07T21:51:53.213795image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-07-07T21:51:54.587641image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-07-07T21:51:56.073781image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-07-07T21:51:57.488630image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-07-07T21:51:59.128883image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-07-07T21:52:00.671734image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-07-07T21:52:02.051666image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-07-07T21:52:03.624263image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-07-07T21:52:05.326177image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-07-07T21:52:06.856899image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-07-07T21:52:08.394680image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-07-07T21:52:09.913096image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-07-07T21:52:11.569714image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-07-07T21:51:48.773745image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-07-07T21:51:50.185444image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-07-07T21:51:51.688407image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-07-07T21:51:53.297104image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-07-07T21:51:54.673818image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-07-07T21:51:56.169502image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-07-07T21:51:57.583866image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-07-07T21:51:59.223577image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-07-07T21:52:00.756374image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-07-07T21:52:02.147423image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-07-07T21:52:03.722557image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-07-07T21:52:05.423011image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-07-07T21:52:06.951595image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-07-07T21:52:08.489717image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-07-07T21:52:09.999522image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-07-07T21:52:11.667167image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-07-07T21:51:48.863992image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-07-07T21:51:50.283737image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-07-07T21:51:51.782294image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-07-07T21:51:53.383976image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-07-07T21:51:54.763443image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-07-07T21:51:56.255575image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-07-07T21:51:57.677828image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-07-07T21:51:59.319013image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-07-07T21:52:00.843309image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-07-07T21:52:02.244296image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-07-07T21:52:03.822148image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-07-07T21:52:05.517437image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-07-07T21:52:07.045492image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-07-07T21:52:08.586795image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-07-07T21:52:10.088052image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-07-07T21:52:11.764079image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-07-07T21:51:48.946235image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-07-07T21:51:50.374166image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-07-07T21:51:51.945512image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-07-07T21:51:53.474106image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-07-07T21:51:54.849451image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-07-07T21:51:56.338994image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-07-07T21:51:57.770644image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-07-07T21:51:59.413392image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-07-07T21:52:00.930914image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-07-07T21:52:02.337135image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-07-07T21:52:04.058975image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-07-07T21:52:05.610175image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-07-07T21:52:07.137489image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-07-07T21:52:08.680610image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-07-07T21:52:10.175927image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-07-07T21:52:11.865630image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-07-07T21:51:49.031413image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-07-07T21:51:50.476801image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-07-07T21:51:52.042767image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-07-07T21:51:53.562491image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-07-07T21:51:54.939253image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-07-07T21:51:56.429722image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-07-07T21:51:57.870731image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-07-07T21:51:59.518354image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-07-07T21:52:01.018829image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-07-07T21:52:02.435237image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-07-07T21:52:04.162716image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-07-07T21:52:05.709858image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-07-07T21:52:07.234400image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-07-07T21:52:08.782818image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-07-07T21:52:10.270892image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-07-07T21:52:11.961497image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-07-07T21:51:49.108829image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-07-07T21:51:50.564761image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-07-07T21:51:52.137823image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-07-07T21:51:53.642744image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-07-07T21:51:55.109955image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-07-07T21:51:56.512835image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-07-07T21:51:57.960430image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-07-07T21:51:59.612659image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-07-07T21:52:01.100384image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-07-07T21:52:02.529019image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-07-07T21:52:04.260387image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-07-07T21:52:05.804605image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-07-07T21:52:07.327850image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-07-07T21:52:08.877325image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-07-07T21:52:10.510471image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-07-07T21:52:12.060986image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-07-07T21:51:49.254207image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-07-07T21:51:50.657657image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-07-07T21:51:52.242307image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-07-07T21:51:53.727050image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-07-07T21:51:55.198142image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-07-07T21:51:56.601184image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-07-07T21:51:58.055964image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-07-07T21:51:59.707316image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-07-07T21:52:01.185715image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-07-07T21:52:02.625118image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-07-07T21:52:04.359070image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-07-07T21:52:05.900079image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-07-07T21:52:07.424532image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-07-07T21:52:08.974912image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-07-07T21:52:10.594896image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-07-07T21:52:12.159068image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-07-07T21:51:49.339887image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-07-07T21:51:50.748430image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-07-07T21:51:52.339108image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-07-07T21:51:53.816293image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-07-07T21:51:55.288801image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-07-07T21:51:56.685874image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-07-07T21:51:58.150674image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-07-07T21:51:59.805598image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-07-07T21:52:01.270648image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-07-07T21:52:02.727076image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-07-07T21:52:04.456966image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-07-07T21:52:05.998618image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-07-07T21:52:07.519002image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-07-07T21:52:09.069412image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-07-07T21:52:10.680530image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-07-07T21:52:12.263493image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-07-07T21:51:49.422523image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-07-07T21:51:50.841220image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-07-07T21:51:52.441114image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-07-07T21:51:53.906410image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-07-07T21:51:55.378359image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-07-07T21:51:56.773873image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-07-07T21:51:58.249547image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-07-07T21:51:59.906383image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-07-07T21:52:01.354956image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-07-07T21:52:02.820259image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-07-07T21:52:04.557581image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-07-07T21:52:06.097767image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-07-07T21:52:07.616293image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-07-07T21:52:09.169327image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-07-07T21:52:10.765816image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-07-07T21:52:12.364563image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-07-07T21:51:49.507410image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-07-07T21:51:50.936597image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-07-07T21:51:52.546589image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-07-07T21:51:53.995012image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-07-07T21:51:55.468990image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-07-07T21:51:56.863963image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-07-07T21:51:58.349586image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-07-07T21:52:00.013250image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-07-07T21:52:01.442419image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-07-07T21:52:02.918232image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-07-07T21:52:04.648711image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-07-07T21:52:06.196959image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-07-07T21:52:07.726018image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-07-07T21:52:09.269843image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-07-07T21:52:10.853336image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-07-07T21:52:12.460274image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-07-07T21:51:49.586394image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-07-07T21:51:51.027750image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-07-07T21:51:52.643827image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-07-07T21:51:54.080350image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-07-07T21:51:55.554333image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-07-07T21:51:56.949716image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-07-07T21:51:58.445199image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-07-07T21:52:00.113878image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-07-07T21:52:01.524251image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-07-07T21:52:03.014840image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-07-07T21:52:04.746231image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-07-07T21:52:06.287639image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-07-07T21:52:07.827819image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-07-07T21:52:09.364681image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-07-07T21:52:10.937311image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-07-07T21:52:12.555100image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-07-07T21:51:49.664380image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-07-07T21:51:51.117748image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-07-07T21:51:52.742695image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-07-07T21:51:54.162017image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-07-07T21:51:55.637985image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-07-07T21:51:57.032504image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-07-07T21:51:58.537531image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-07-07T21:52:00.208720image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-07-07T21:52:01.603416image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-07-07T21:52:03.111395image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-07-07T21:52:04.842319image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-07-07T21:52:06.380125image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-07-07T21:52:07.915953image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-07-07T21:52:09.456070image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-07-07T21:52:11.017938image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-07-07T21:52:12.655238image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-07-07T21:51:49.743840image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-07-07T21:51:51.207182image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-07-07T21:51:52.837656image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-07-07T21:51:54.240967image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-07-07T21:51:55.721857image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-07-07T21:51:57.119670image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-07-07T21:51:58.629590image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-07-07T21:52:00.303665image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-07-07T21:52:01.683817image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-07-07T21:52:03.212814image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-07-07T21:52:04.937164image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-07-07T21:52:06.472266image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-07-07T21:52:08.010468image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-07-07T21:52:09.546962image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-07-07T21:52:11.103295image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-07-07T21:52:12.761029image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-07-07T21:51:49.832856image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-07-07T21:51:51.302968image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-07-07T21:51:52.936441image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-07-07T21:51:54.326094image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-07-07T21:51:55.811464image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-07-07T21:51:57.205341image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-07-07T21:51:58.724858image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-07-07T21:52:00.401982image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-07-07T21:52:01.772846image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-07-07T21:52:03.312440image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-07-07T21:52:05.037015image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-07-07T21:52:06.569832image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-07-07T21:52:08.109362image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-07-07T21:52:09.642095image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-07-07T21:52:11.185638image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-07-07T21:52:12.854371image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-07-07T21:51:49.917419image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-07-07T21:51:51.399391image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-07-07T21:51:53.040481image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-07-07T21:51:54.412721image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-07-07T21:51:55.901758image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-07-07T21:51:57.297096image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-07-07T21:51:58.935737image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-07-07T21:52:00.503282image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-07-07T21:52:01.859575image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-07-07T21:52:03.413237image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-07-07T21:52:05.135974image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-07-07T21:52:06.668073image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-07-07T21:52:08.208066image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-07-07T21:52:09.740877image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-07-07T21:52:11.272475image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Missing values

2024-07-07T21:52:12.962456image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-07-07T21:52:14.394699image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2024-07-07T21:52:17.774618image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

addr_stateannual_incapplication_typeavg_cur_balbc_utilchargeoff_within_12_mthsdelinq_2yrsdtiemp_lengthemp_titlefico_range_highfico_range_lowgradehome_ownershipinq_last_6mthsinstallmentint_rateissue_dlast_fico_range_highlast_fico_range_lowloan_amntloan_statuspurposerevol_balrevol_utilsub_gradetermverification_status
42536NC60000.0Individual476.015.90.00.04.624 yearsProject Manager724.0720.0BRENT1.0392.810.10992013-12-01569.0565.012000.0Fully Paiddebt_consolidation7137.00.240B236 monthsNot Verified
42537TX39600.0Individual1379.016.10.00.02.492 yearsSurgical Technician759.0755.0BMORTGAGE2.0157.130.10992013-12-01534.0530.04800.0Fully Paidhome_improvement4136.00.161B236 monthsSource Verified
42538MI55000.0Individual9570.053.90.00.022.8710+ yearsTeam Leadern Customer Ops & Systems734.0730.0BOWN0.0885.460.10992013-12-01834.0830.027050.0Fully Paiddebt_consolidation36638.00.612B236 monthsVerified
42539TX96500.0Individual11783.083.50.00.012.613 yearsSystems Engineer709.0705.0AMORTGAGE0.0373.940.07622013-12-01809.0805.012000.0Fully Paiddebt_consolidation13248.00.557A336 monthsNot Verified
42540NC88000.0Individual2945.087.70.01.010.024 yearsAssistant Director - Human Resources674.0670.0BRENT0.0470.710.12852013-12-01569.0565.014000.0Fully Paiddebt_consolidation3686.00.819B436 monthsNot Verified
42541CT105000.0Individual26765.025.00.00.014.0510+ yearsMANAGER INFORMATION DELIVERY764.0760.0AMORTGAGE1.0368.450.06622013-12-01779.0775.012000.0Fully Paiddebt_consolidation13168.00.216A236 monthsNot Verified
42542FL63000.0Individual38927.079.10.00.016.512 yearsaircraft maintenance engineer674.0670.0AMORTGAGE0.0476.300.08902013-12-01749.0745.015000.0Fully Paiddebt_consolidation11431.00.742A536 monthsNot Verified
42543CA28000.0Individual1440.096.00.00.08.403 yearsSpecial Order Fulfillment Clerk664.0660.0CRENT0.0266.340.16242013-12-01634.0630.07550.0Fully Paiddebt_consolidation5759.00.720C536 monthsNot Verified
42544CA325000.0Individual53306.067.10.00.018.555 yearsArea Sales Manager749.0745.0AMORTGAGE1.0872.520.07622013-12-01789.0785.028000.0Fully Paiddebt_consolidation29581.00.546A336 monthsSource Verified
42545CO130000.0Individual36362.093.00.00.013.0310+ yearsLTC719.0715.0BMORTGAGE1.0398.520.11992013-12-01714.0710.012000.0Fully Paiddebt_consolidation10805.00.670B336 monthsSource Verified
addr_stateannual_incapplication_typeavg_cur_balbc_utilchargeoff_within_12_mthsdelinq_2yrsdtiemp_lengthemp_titlefico_range_highfico_range_lowgradehome_ownershipinq_last_6mthsinstallmentint_rateissue_dlast_fico_range_highlast_fico_range_lowloan_amntloan_statuspurposerevol_balrevol_utilsub_gradetermverification_status
2856439CA60000.0Individual6463.071.20.00.028.3410+ yearsHousekeeping769.0765.0BRENT0.0576.090.09442017-06-01829.0825.018000.0Fully Paiddebt_consolidation15316.00.417B136 monthsNot Verified
2856440NJ200000.0Individual71965.078.80.02.024.4410+ yearsOWNER694.0690.0BMORTGAGE0.0980.890.10912017-06-01734.0730.030000.0Fully Paiddebt_consolidation39843.00.715B436 monthsVerified
2856442CO89000.0Individual27498.076.20.00.018.8810+ yearsSuperintendent744.0740.0AMORTGAGE0.0869.050.07352017-06-01814.0810.028000.0Fully Paiddebt_consolidation28422.00.534A436 monthsNot Verified
2856443KS180000.0Individual31465.094.80.00.016.5410+ yearsPartner709.0705.0BMORTGAGE0.0251.680.09442017-06-01739.0735.012000.0Fully Paiddebt_consolidation28105.00.672B160 monthsSource Verified
2856444CA125000.0Individual4763.066.00.01.08.7010+ yearsassociate advisor689.0685.0ARENT3.0670.410.07352017-06-01699.0695.021600.0Fully Paidcredit_card28598.00.620A436 monthsVerified
2856447FL110000.0Individual25116.087.30.01.021.112 yearstech data author689.0685.0CMORTGAGE2.01094.120.13592017-06-01684.0680.032200.0Fully Paiddebt_consolidation31457.00.914C236 monthsVerified
2856449CA240000.0Individual62430.059.80.00.012.192 yearsVice President Quality729.0725.0BMORTGAGE1.01128.210.09932017-06-01729.0725.035000.0Fully Paidcredit_card44329.00.577B236 monthsSource Verified
2856450PA100000.0Individual58595.062.50.00.07.8410+ yearsRegional General Manager694.0690.0AMORTGAGE0.0156.620.07972017-06-01669.0665.05000.0Fully Paidmajor_purchase11750.00.527A536 monthsVerified
2856451OH40000.0Individual2360.033.40.00.025.8610+ yearsClerk779.0775.0BRENT0.0340.880.10422017-06-01804.0800.010500.0Fully Paidcredit_card13285.00.319B336 monthsSource Verified
2856452CA32000.0Individual2573.00.00.00.027.57NaNNaN684.0680.0BOWN0.0193.410.09932017-06-01539.0535.06000.0Non-Performingdebt_consolidation2855.00.127B236 monthsVerified